library(httr)
library(jsonlite)
library(tidyverse)
getCarpobrotusObservations <- function(place_id, taxon_id, quality_grade){
total_results = NULL
page = 1
delay = 1.0
results = tibble()
while(is.null(total_results) || nrow(results) < total_results) {
call_url <- str_glue('https://api.inaturalist.org/v1/observations?',
'place_id={place_id}&taxon_id={taxon_id}',
'&captive=false&geoprivacy=open',
'&quality_grade={quality_grade}',
'&per_page=200&page={page}')
get_json_call <- GET(url = call_url) %>%
content(as = "text") %>% fromJSON(flatten = TRUE)
if (!is.null(get_json_call)) {
if (is.null(total_results)) {
total_results <- get_json_call$total_results # number of results of the call
}
results_i <- as_tibble(get_json_call$results) %>%
select(taxon.name, taxon.rank, identifications_count,
created_at, observed_on,
geojson.coordinates, positional_accuracy,
user.login, user.id, user.name, user.observations_count,
user.identifications_count, user.activity_count,
license_code, num_identification_agreements, uri) %>%
unnest_wider(geojson.coordinates, names_sep = "_") %>%
rename(longitude=geojson.coordinates_1, latitude=geojson.coordinates_2)
results <- rbind(results, results_i)
page <- page + 1
Sys.sleep(delay)
}
}
return(results)
}
datos_carpobrotus <- getCarpobrotusObservations(place_id=7259,
taxon_id=49322,
quality_grade='research')
datos_carpobrotus <- datos_carpobrotus %>% filter(created_at<='2023-06-09')Supplementary Information
Status of the invasion of Carpobrotus edulis in Uruguay based on community science records
Carpobrotus edulis
Data download
To download the data we used the iNaturalist API, considering:
- ‘Uruguay’ as location:
place_id=7259 - ‘Carpobrotus edulis’ as taxon:
taxon_id=49322 - ‘Research grade’ as data quality assessment:
quality_grade=research
Download date
9th of June, 2023
Spatial and temporal analyses
Data preparation
To download the polygon for Uruguay, I used the geouy package, with geouy::load_geouy('Dptos'). I saved this object to avoid downloading it manually again. The CRS of the sf object is: EPSG:32721. I also used the package geonames to get the department (stateProvince) for each record.
Code
library(lubridate)
library(geonames)
options(geonamesUsername="biodiversidata") # A (free) username is required and rate limits exist
library(sf)
sf::sf_use_s2(FALSE)
# options
options(scipen = 999)
uruguay <- readRDS('data/Uruguay.rds')
deptos_costeros <- c('MONTEVIDEO','MALDONADO','CANELONES', 'ROCHA')
costa_uruguay <- uruguay %>% filter(nombre %in% deptos_costeros)
costa_entera_uruguay <- uruguay %>% filter(nombre %in% c(deptos_costeros, 'SAN JOSÉ'))
localidades <- readRDS('data/localidades.RDS')
localidades_costa <- localidades %>% filter(NOMBDEPTO %in% deptos_costeros)
areas_protegidas <- read_sf('data/areas_protegidas/c397Polygon.shp', options = 'ENCODING=WINDOWS-1252') %>%
st_transform(32721) %>% st_cast()
areas_protegidas_costa <- areas_protegidas %>%
filter(nombre %in% c('Cerro Verde', 'Cabo Polonio', 'Laguna de Rocha', 'Laguna Garzón', 'Isla de Flores', 'Humedales del Santa Lucla'))
getStateProvince <- function(lat, lng){
subdivision <- try(GNcountrySubdivision(lat, lng, radius = "1", maxRows = 1), silent = TRUE)
Sys.sleep(1.0)
if(class(subdivision)=='try-error'){
subdivision$adminName1 <- NA
}
else if (length(subdivision$adminName1)==0){
subdivision$adminName1 <- NA
}
return(subdivision$adminName1)
}
# stateProvince of earch record
datos_carpobrotus<- datos_carpobrotus %>%
mutate(stateProvince=map2_chr(latitude, longitude, getStateProvince))
datos_carpobrotus <- datos_carpobrotus %>%
mutate(stateProvince =case_when(stateProvince=='Rocha Department' ~ 'Rocha',
stateProvince=='Montevideo Department' ~ 'Montevideo',
TRUE ~ stateProvince))
# estación del año
datos_carpobrotus<- datos_carpobrotus %>%
mutate(observed_on=as_date(observed_on)) %>%
mutate(season=lubridate::quarter(observed_on)) %>%
mutate(season=ifelse(season==1, 'summer',
ifelse(season==2, 'autumn',
ifelse(season==3, 'winter', 'spring'))))
saveRDS(datos_carpobrotus, 'data/datos_carpobrotus.rds')
write_excel_csv(datos_carpobrotus, 'data/datos_carpobrotus.csv', na = '')Spatial coverage
Code
library(tmap)
tmap_mode("view")
sf_carpobrotus <- datos_carpobrotus %>%
st_as_sf(coords = c("longitude", "latitude")) %>%
st_set_crs(4326) %>%
st_transform(32721)
tm_graticules(alpha = 0.3) +
tm_shape(areas_protegidas_costa) +
tm_fill('darkgreen', alpha = 0.4) +
tm_shape(localidades_costa %>% filter(AREA>5000000)) +
tm_fill(col='blue', alpha = 0.4) +
tm_shape(sf_carpobrotus) +
tm_dots(alpha = 0.4)Code
mapa.carpobrotus <- tm_graticules(alpha = 0.3) +
tm_shape(uruguay, bbox = costa_entera_uruguay) +
tm_fill(col='grey97') +
tm_borders(col='grey80', alpha = 0.4) +
tm_shape(costa_uruguay) +
tm_fill(col='grey90') +
tm_borders(col='grey60', alpha = 0.4) +
tm_shape(st_intersection(areas_protegidas_costa, uruguay)) +
tm_fill('darkgreen', alpha = 0.4) +
tm_shape(localidades_costa %>% filter(AREA>5000000)) +
tm_fill(col='blue', alpha = 0.4) +
tm_shape(sf_carpobrotus) +
tm_dots(alpha = 0.4) +
tm_layout(scale = 2)
tmap_save(mapa.carpobrotus, 'figs/mapa.carpobrotus.svg')Departments
Code
library(knitr)
datos_carpobrotus %>%
group_by(stateProvince) %>%
count() %>% rename(Department=stateProvince,
`Number of records`=n) %>%
kable()| Department | Number of records |
|---|---|
| Canelones | 46 |
| Maldonado | 98 |
| Montevideo | 12 |
| Rocha | 48 |
Protected areas
Code
carpobrotus_areas_protegidas <- st_join(areas_protegidas_costa,
sf_carpobrotus) %>%
group_by(id, nombre) %>%
summarise(NR=ifelse(n_distinct(taxon.name, na.rm=T)!=0, n(), 0),
presence=ifelse(NR>0, 1, 0)) %>%
st_cast()
mapa.carpobrotus.areas_protegidas <- tm_graticules(alpha = 0.3) +
tm_shape(uruguay, bbox = costa_entera_uruguay) +
tm_fill(col='grey97') +
tm_borders(col='grey80', alpha = 0.4) +
tm_shape(costa_uruguay) +
tm_fill(col='grey90') +
tm_borders(col='grey60', alpha = 0.4) +
tm_shape(carpobrotus_areas_protegidas) +
tm_fill('presence', alpha = 0.4, style = 'cat', palette = 'Set1')
mapa.carpobrotus.areas_protegidasTemporal coverage
A total of 78 users recorded 204 observations of Carpobrotus edulis. The first records is from 2008-03-16 and the last is from 2023-05-10.
Seasons
Code
library(patchwork)
timeline.plot <- datos_carpobrotus %>%
add_count(taxon.name, year=year(observed_on),
name='records_per_year') %>%
ggplot(., aes(x=observed_on, y=records_per_year)) +
geom_line(show.legend = FALSE, linewidth=1) +
scale_x_date(date_breaks = "1 year", date_labels = "%Y") +
theme_bw()+
labs(x='', y= 'Number of records')
season.year.plot <- datos_carpobrotus %>%
add_count(taxon.name, season, name='records_per_season') %>%
mutate(season=factor(season,
levels = c('summer', 'autumn', 'winter', 'spring'))) %>%
ggplot(aes(x=season, y=observed_on)) +
geom_jitter(aes(col = season), width = 0.01, show.legend = FALSE) +
stat_summary(fun = mean, fun.min = min, fun.max = max) +
theme_bw() +
labs(x='', y= 'Date')
season.n.plot <- datos_carpobrotus %>%
add_count(taxon.name, season, name='records_per_season') %>%
mutate(season=factor(season,
levels = c('summer', 'autumn', 'winter', 'spring'))) %>%
ggplot(aes(x=season, y=records_per_season)) +
geom_segment(aes(x=season, xend=season, y=0,
yend=records_per_season, col=season), show.legend = FALSE) +
geom_point(aes(col=season), show.legend = FALSE) +
theme_bw() +
labs(x='', y= 'Number of records')
timeline.plot / (season.year.plot | season.n.plot)
Code
ggsave(timeline.plot, filename='figs/timeline.plot.svg', device = 'svg', width=6, height=3, dpi=300)
ggsave(season.n.plot, filename='figs/season.n.plot.svg', device = 'svg', width=3, height=3, dpi=300)Photographic analyses
We assessed the following attributes:
- Density: high, medium, low
- Phenology: no evidence of flowering/fruiting, flowering, flower budding , fruiting, flowering/flower budding, flowering/fruiting
- Presence of humann infraestructure: present, absent
Code
photo_evaluation <- read_csv('data/photo_evalutaion.csv')
photo_evaluation_carpobrotus <- left_join(datos_carpobrotus,
photo_evaluation %>% select(uri, density, phenology, infrastructure)) %>%
mutate(density = case_when(density=='alta'|density=='alta/media' ~ 'high',
density=='media'|density=='media/alta' ~ 'medium',
density=='baja' ~ 'low',
is.na(density) ~ 'not assessed')) %>%
mutate(phenology=ifelse(phenology=='No evidence of flowering',
'no evidence of flowering/fruiting', phenology)) %>%
mutate(infrastructure = case_when(infrastructure==0 ~ 'absent',
infrastructure==1 ~ 'present',
is.na(infrastructure) ~ 'not assessed')) %>%
mutate(density=fct_relevel(density, c('high', 'medium', 'low', 'not assessed')))
sf_photo_evaluation_carpobrotus <- photo_evaluation_carpobrotus %>%
st_as_sf(coords = c("longitude", "latitude")) %>%
st_set_crs(4326) %>%
st_transform(32721)Density
Code
photo_evaluation_carpobrotus %>%
group_by(density) %>%
summarise(count = n()) %>%
mutate(freq = scales::label_percent()(count / sum(count))) %>%
arrange(desc(count)) %>%
rename(Density=density, N=count, `%`=freq) %>%
kable()| Density | N | % |
|---|---|---|
| high | 75 | 36.8% |
| medium | 67 | 32.8% |
| not assessed | 33 | 16.2% |
| low | 29 | 14.2% |
Code
tmap_mode("view")
# interactivos
tm_graticules(alpha = 0.3) +
tm_shape(localidades_costa %>% filter(AREA>5000000)) +
tm_fill(col='grey10', alpha = 0.4) +
tm_shape(sf_photo_evaluation_carpobrotus %>%
mutate(density=ifelse(density=='not assessed', NA, density))) +
tm_dots(col = 'density', title = 'Density',
palette = 'PRGn', showNA=F, labels=c('high', 'medium', 'low')) +
tm_shape(costa_uruguay) +
tm_borders(col='grey60', alpha = 0.4)Phenology
Code
photo_evaluation_carpobrotus %>%
group_by(phenology) %>%
summarise(count = n()) %>%
mutate(freq = scales::label_percent()(count / sum(count))) %>%
arrange(desc(count)) %>%
rename(Phenology=phenology, N=count, `%`=freq) %>%
kable()| Phenology | N | % |
|---|---|---|
| no evidence of flowering/fruiting | 117 | 57.35% |
| fruiting | 42 | 20.59% |
| flowering | 27 | 13.24% |
| flowering/flower budding | 11 | 5.39% |
| flowering/fruiting | 4 | 1.96% |
| flower budding | 3 | 1.47% |
Code
phenology.plot <- photo_evaluation_carpobrotus %>%
filter(!phenology %in% c('flower budding', 'flowering/fruiting' )) %>%
add_count(phenology, month=(month(observed_on)),
name='records_per_month') %>%
ggplot(., aes(x=(month), #strftime(observed_on, format="%b"),
y=records_per_month,
col=phenology)) +
geom_line(size=2) +
scale_color_brewer(palette='Set2') +
scale_x_continuous(breaks= c(1,2,3,4,5,6,7,8,9,10,11,12),
labels = c('Jan', 'Feb', 'Mar',
'Apr','May','Jun','Jul',
'Aug','Sep','Oct','Nov','Dec'))+
#facet_wrap(~phenology, scales = 'free_y') +
theme_bw() +
labs(x='Months', y= 'Number of records', col='Phenology')
phenology.plot
Code
ggsave(phenology.plot, filename='figs/phenology.plot.svg', device = 'svg', width=7.2, height=5.2, dpi=300)Infraestructure
Code
photo_evaluation_carpobrotus %>%
group_by(infrastructure) %>%
summarise(count = n()) %>%
mutate(freq = scales::label_percent()(count / sum(count))) %>%
arrange(desc(count)) %>%
rename(Infrastructure=infrastructure, N=count, `%`=freq) %>%
kable()| Infrastructure | N | % |
|---|---|---|
| absent | 120 | 58.8% |
| present | 46 | 22.5% |
| not assessed | 38 | 18.6% |
Code
tmap_mode("view")
tm_graticules(alpha = 0.3) +
tm_shape(localidades_costa %>% filter(AREA>5000000)) +
tm_fill(col='grey10', alpha = 0.4) +
tm_shape(sf_photo_evaluation_carpobrotus) +
tm_dots(col = 'infrastructure', title = 'Infrastructure',
palette = 'Set1', labels=c('absent', 'present','not assessed')) +
tm_shape(costa_uruguay) +
tm_borders(col='grey60', alpha = 0.4)All records on NaturalistaUY
Data download (Montevideo, Canelones, Maldonado and Rocha)
Because iNat’s API doesn’t allow to download more than 10,000 records, we downloaded the data directly from the website naturalista.uy/observations/export. We considered:
- ‘Montevideo’, ‘Canelones’, ‘Maldonado’ and ‘Rocha’ as location:
place_id=12416,place_id=12410,place_id=12415,place_id=12420 - ‘FALSE’ as captive/cultivated quality assessment:
captive=false - ‘open’ as geoprivacy:
geoprivacy=open
Download date
9th of June, 2023
Code
allObservations <- read_csv('data/observations-334644.csv', guess_max = 72000)
allObservations <- allObservations %>%
filter(coordinates_obscured==FALSE &
!is.na(taxon_species_name) &
captive_cultivated == FALSE) %>%
select(kingdom=taxon_kingdom_name, phylum=taxon_phylum_name,
class=taxon_class_name, order=taxon_order_name,
family=taxon_family_name, genus=taxon_genus_name,
species=taxon_species_name, scientific_name,
quality_grade, observed_on, user_login, user_id,
state_province=place_admin1_name,
longitude, latitude)
# filter plant data
all_records_time <- left_join(
datos_carpobrotus %>% mutate(year=year(observed_on)) %>%
add_count(taxon.name, year=year(observed_on),
name='records_per_year') %>%
select(observed_on, year, carpobrotus_records_per_year= records_per_year),
allObservations %>% mutate(year=year(observed_on)) %>%
filter(kingdom=='Plantae') %>%
add_count(year=year(observed_on),
name='records_per_year') %>%
select(year, all_records_per_year=records_per_year) %>%
distinct(year, all_records_per_year)
)Temporal coverage
Code
coef <- 100
double_temporal_plot <- ggplot(data=all_records_time %>% filter(observed_on<'2022-12-31'),
aes(x=observed_on)) +
geom_line(aes(y=carpobrotus_records_per_year, col='Carpobrotus'), size=1) +
geom_line(aes(y=all_records_per_year/coef, col='Plantae'), size=1) +
scale_y_continuous(name = expression(paste('Number of', italic('Carpobrotus edulis'), 'records')),
sec.axis = sec_axis(~.*coef,
name= 'Number of Plant records')) +
scale_x_date(date_breaks = "1 year", date_labels = "%Y") +
theme_bw() + labs(x='', col='')
double_temporal_plot
Code
ggsave(double_temporal_plot, filename='figs/double.temporal.plot.svg', device = 'svg', dpi=300)Spatial analyses
Creation of grid cell (10x10km)
allObservations_sf <- allObservations %>%
st_as_sf(coords = c("longitude", "latitude")) %>%
st_set_crs(4326) %>%
st_transform(32721)
costa_uruguay_grillas <- st_make_grid(st_union(costa_uruguay), 10000) %>%
st_intersection(st_union(costa_uruguay)) %>%
st_sf(gridID=1:length(.), geometry= .) %>%
st_make_valid() %>% st_cast() Intersection of grid cells with all the data and the data of Caprobrotus edulis
grillas_allObservations <- st_join(costa_uruguay_grillas,
allObservations_sf) %>%
group_by(gridID) %>%
summarise(NR=ifelse(n_distinct(species, na.rm=T)!=0, n(), 0),
SR=n_distinct(species, na.rm = T),
spsList = paste(species, collapse = ';')) %>%
st_cast()
grillas_carpobrotus <- st_join(costa_uruguay_grillas, sf_carpobrotus) %>%
group_by(gridID) %>%
summarise(NR=ifelse(n_distinct(taxon.name, na.rm=T)!=0, n(), 0)) %>%
st_cast()Estimation of sampling effort per grid cell
We used the function get_gridsSlopes() created by Biodiversidata (Grattarola et al. 2020).
More info about this analysis
The code is available at the GitHub repo: Multiple forms of hotspots of tetrapod biodiversity and the challenges of open-access data scarcity.
To identify the areas of ignorance we quantified the levels of inventory incompleteness for each group by using curvilinearity of smoothed species accumulation curves (SACs). This method assumes that SACs of poorly sampled grids tend towards a straight line, while those of better sampled ones have a higher degree of curvature. As a proxy for inventory incompleteness we calculated the degree of curvilinearity as the mean slope of the last 10% of SACs.
Code
# The function ```get_gridsSlopes``` finds a species accumulation curve (SAC) for each grid-cell using the method ‘exact’ of the function ```specaccum``` of the vegan package and then calculates the degree of curvilinearity as the mean slope of the last 10% of the curve.
library(vegan)
library(spaa)
get_gridsSlopes <- function(data_abundance){
gridSlope <- data.frame(gridID=integer(), slope=numeric(), stringsAsFactors=FALSE)
data_abundance <- as.data.frame(data_abundance)
data_abundance$abundance <- as.integer(1)
cells <- unique(data_abundance$gridID)
splistT <- list()
spaccum <- list()
slope <- list()
for (i in cells) {
splist <- data_abundance[data_abundance$gridID == i,c(2:4)]
splistT[[i]] = data2mat(splist)
spaccum[[i]] = specaccum(splistT[[i]], method = "exact")
slope[[i]] = (spaccum[[i]][[4]][length(spaccum[[i]][[4]])]-
spaccum[[i]][[4]][ceiling(length(spaccum[[i]][[4]])*0.9)])/
(length(spaccum[[i]][[4]])- ceiling(length(spaccum[[i]][[4]])*0.9))
gridSlope_i <- data.frame(gridID=i, slope=slope[[i]], stringsAsFactors=FALSE)
gridSlope <- rbind(gridSlope, gridSlope_i)
}
gridSlope <- gridSlope %>% as_tibble() %>%
mutate(slope=ifelse(is.nan(slope), NA, slope))
return(gridSlope)
}
allObservations.SACs <- grillas_allObservations %>% as_tibble() %>%
mutate(species=str_split(spsList, ';')) %>%
unnest(species) %>%
group_by(spsList) %>% mutate(sample = row_number()) %>%
ungroup() %>%
mutate(sample=ifelse(is.na(species), 0 , sample)) %>%
select(gridID, sample, species)
allObservations.incompleteness <- get_gridsSlopes(allObservations.SACs)Spatian join
All the data per grid cell: total number of records, species richness, incompleteness curve (national sampling effort), and number of records of Carpobrotus edulis.
costa_uruguay.incompleteness <- left_join(grillas_allObservations,
allObservations.incompleteness) %>%
left_join(., grillas_carpobrotus %>%
rename(carpobrotus=NR) %>%
st_drop_geometry())Sampling bias
Maps
Correlations (non-spatial)
Code
library(ggpubr)
tabla.final <- costa_uruguay.incompleteness %>%
st_drop_geometry() %>%
select(grilla=gridID, NR, SR, carpobrotus, slope)
summary(lm(carpobrotus ~ NR, data=tabla.final))
Call:
lm(formula = carpobrotus ~ NR, data = tabla.final)
Residuals:
Min 1Q Median 3Q Max
-13.3254 -0.1541 0.0797 0.1381 23.3044
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.1381389 0.1583721 -0.872 0.384
NR 0.0073048 0.0005093 14.342 <0.0000000000000002 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.365 on 259 degrees of freedom
Multiple R-squared: 0.4426, Adjusted R-squared: 0.4405
F-statistic: 205.7 on 1 and 259 DF, p-value: < 0.00000000000000022Code
nr.caprobrotus <- tabla.final %>% filter(carpobrotus>0) %>%
ggplot(aes(x=NR, y=carpobrotus)) +
geom_jitter() +
geom_smooth(method = 'lm') +
stat_regline_equation(label.y = 30, aes(label = ..eq.label..)) +
stat_regline_equation(label.y = 28, aes(label = ..adj.rr.label..)) +
labs(x='Number of records per grid cell', y='Caprobrotus edulis records per grid cell') +
theme_bw()
summary(lm(carpobrotus ~ slope, data=tabla.final))
Call:
lm(formula = carpobrotus ~ slope, data = tabla.final)
Residuals:
Min 1Q Median 3Q Max
-5.5117 -1.6170 -0.3330 0.8455 29.9189
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 7.202 1.004 7.172 0.000000000044 ***
slope -8.586 1.404 -6.118 0.000000009632 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 3.788 on 135 degrees of freedom
(124 observations deleted due to missingness)
Multiple R-squared: 0.2171, Adjusted R-squared: 0.2113
F-statistic: 37.43 on 1 and 135 DF, p-value: 0.000000009632Code
incompleteness.caprobrotus <- tabla.final %>% filter(carpobrotus>0) %>%
ggplot(aes(x=slope, y=carpobrotus)) +
geom_point() + geom_smooth(method = 'lm') +
stat_regline_equation(label.y = 30, label.x = 0.65, aes(label = ..eq.label..)) +
stat_regline_equation(label.y = 28, label.x = 0.65, aes(label = ..adj.rr.label..)) +
labs(x='Sampling incompleteness per grid cell', y='Caprobrotus edulis records per grid cell') +
theme_bw()
nr.caprobrotus / incompleteness.caprobrotus
Code
ggsave(nr.caprobrotus | incompleteness.caprobrotus, filename='figs/correlation.plot.svg', device = 'svg', width=9.2, height=5.2, dpi=300)Spatial correlations
Code
library(SpatialPack)
XY_costa_uruguay.incompleteness <- st_centroid(costa_uruguay.incompleteness) %>%
st_coordinates() %>% as_tibble()
costa_uruguay.incompleteness.cor <- modified.ttest(costa_uruguay.incompleteness$NR,
costa_uruguay.incompleteness$carpobrotus,
XY_costa_uruguay.incompleteness)
CORR_NR <- tibble(Cor = 'total number of records vs records of C. edulis',
R = costa_uruguay.incompleteness.cor$corr,
p.value = costa_uruguay.incompleteness.cor$p.value,
Fstat = costa_uruguay.incompleteness.cor$Fstat,
dof = costa_uruguay.incompleteness.cor$dof)
CORR_NR %>%
kable(digits = 3)| Cor | R | p.value | Fstat | dof |
|---|---|---|---|---|
| total number of records vs records of C. edulis | 0.665 | 0 | 0.794 | 153.634 |
Code
demografia_hogares <- readRDS('data/demografia_hogares.rds')
pop_por_grilla <- st_join(costa_uruguay_grillas, demografia_hogares) %>%
group_by(gridID) %>%
summarise(pop=sum(h_tot, na.rm = T)) %>%
st_cast()
pop_por_grilla.cor <- modified.ttest(costa_uruguay.incompleteness$NR,
pop_por_grilla$pop,
XY_costa_uruguay.incompleteness)
CORR_POP <- tibble(Cor = 'number of inhabitants vs records of C. edulis',
R = pop_por_grilla.cor$corr,
p.value = pop_por_grilla.cor$p.value,
Fstat = pop_por_grilla.cor$Fstat,
dof = pop_por_grilla.cor$dof)
CORR_POP %>%
kable(digits = 3)| Cor | R | p.value | Fstat | dof |
|---|---|---|---|---|
| number of inhabitants vs records of C. edulis | 0.299 | 0.012 | 0.099 | 67.403 |
Code
tmap_mode("view")
tm_graticules(alpha = 0.3) +
tm_shape(pop_por_grilla) +
tm_fill(col='pop', n = 6, style='jenks', title='Population') +
tm_shape(costa_uruguay) +
tm_borders(col='grey60', alpha = 0.4)Final table
Data per grid cell:
- Number of records: total number of records on NaturalistaUY (all species)
- Species richness: total number of species on NaturalistaUY
- Carpobrotus records: number of records of Carpobrotus edulis on NaturalistaUY
- Incompleteness: slope of the SAC, a measure of sampling effort (values close to 0 indicate well sampled grid cells)
Code
tabla.final %>% filter(carpobrotus>0) %>% arrange(slope) %>%
select(`Number of records`=NR,
`Species richness`=SR,
`Carpobrotus record`=carpobrotus,
Incompleteness=slope) %>%
kable(digits = 3)| Number of records | Species richness | Carpobrotus record | Incompleteness |
|---|---|---|---|
| 1980 | 792 | 1 | 0.204 |
| 1507 | 620 | 2 | 0.219 |
| 992 | 424 | 10 | 0.246 |
| 1620 | 691 | 35 | 0.247 |
| 1093 | 493 | 13 | 0.267 |
| 1213 | 572 | 8 | 0.274 |
| 1040 | 493 | 14 | 0.289 |
| 1066 | 508 | 13 | 0.293 |
| 996 | 541 | 7 | 0.312 |
| 1297 | 681 | 4 | 0.335 |
| 635 | 345 | 3 | 0.345 |
| 480 | 250 | 1 | 0.355 |
| 1001 | 568 | 11 | 0.370 |
| 735 | 433 | 19 | 0.393 |
| 443 | 258 | 6 | 0.400 |
| 347 | 205 | 4 | 0.410 |
| 481 | 304 | 8 | 0.453 |
| 326 | 216 | 1 | 0.461 |
| 419 | 267 | 8 | 0.461 |
| 202 | 130 | 1 | 0.462 |
| 122 | 82 | 4 | 0.471 |
| 547 | 365 | 3 | 0.481 |
| 254 | 184 | 4 | 0.566 |
| 239 | 179 | 4 | 0.604 |
| 159 | 122 | 2 | 0.615 |
| 103 | 85 | 3 | 0.698 |
| 89 | 80 | 1 | 0.815 |